A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
| Autor: | Zhencheng Fang, Hongwei Zhou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
General Chemical Engineering media_common.quotation_subject computer.software_genre Machine learning General Biochemistry Genetics and Molecular Biology Annotation Deep Learning Humans Function (engineering) media_common General Immunology and Microbiology business.industry General Neuroscience Deep learning Construct (python library) Statistical classification ComputingMethodologies_PATTERNRECOGNITION Virtual machine Metagenome Algorithm design Metagenomics Artificial intelligence business computer Host (network) Algorithms |
| Zdroj: | Journal of Visualized Experiments. |
| ISSN: | 1940-087X |
| DOI: | 10.3791/62250 |
| Popis: | A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are expected processes in many metagenomic data analyses. Since metagenomic data contain a large number of novel species and genes, high-performing classification algorithms are needed in many studies. Biologists often encounter challenges in finding suitable sequence classification and annotation tools for a specific task and are often not able to construct a corresponding algorithm on their own because of a lack of the necessary mathematical and computational knowledge. Deep learning techniques have recently become a popular topic and show strong advantages in many classification tasks. To date, many highly packaged deep learning packages, which make it possible for biologists to construct deep learning frameworks according to their own needs without in-depth knowledge of the algorithm details, have been developed. In this tutorial, we provide a guideline for constructing an easy-to-use deep learning framework for sequence classification without the need for sufficient mathematical knowledge or programming skills. All the code is optimized in a virtual machine so that users can directly run the code using their own data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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